This R package implements an approach to estimating the causal effect of a designed intervention on a time series. For example, how many additional daily clicks were generated by an advertising campaign? Answering a question like this can be difficult when a randomized experiment is not available. The package overcomes this difficulty using a structural Bayesian time-series model to estimate how the response metric would have evolved after the intervention if the intervention had not occurred.
install.packages("devtools")
library(devtools)
devtools::install_github("google/CausalImpact")
library(CausalImpact)- Manuscript: Brodersen et al. (under review)
- Discussion forum: https://groups.google.com/d/forum/CausalImpact